Mathematical Methods for Spatially Cohesive Reserve Design

The problem of designing spatially cohesive nature reserve systems that meet biodiversity objectives is formulated as a nonlinear integer programming problem. The multiobjective function minimises a combination of boundary length, area and failed representation of the biological attributes we are trying to conserve. The task is to reserve a subset of sites that best meet this objective. We use data on the distribution of habitats in the Northern Territory, Australia, to show how simulated annealing and a greedy heuristic algorithm can be used to generate good solutions to such large reserve design problems, and to compare the effectiveness of these methods.     

模糊聚类分析法对哈密地下水监测网的优化研究

试用模糊聚类分析法将哈密地下水监测网点从原来的８个点优化为６个点．经研究较符合实际情况，实践研究了优化方法．     

Landscape Change and Ecosystem Classification in a Municipal District of a Small City (Isernia,Central Italy)

Landscape changes taking place from 1954 to 1992 in the muncipal district of Isernia city (Central Italy) were described in relation to a system of ecosystem classification. Isernia municipal district was selected for study because recent historic changes in this area represent a typical example of landscape transformation similar to many small cities of Italy and other Mediterranean countries. To assess overall changes, three land cover maps (scale 1:25,000) were derived from panchromatic aerial photographs and field surveys. These were then digitalised in a Geographic Information System. A Land Facet (LF) map was derived by combining a phytoclimatic, a lithostatigrafic and a topographic map, and then digitalised as data layers in the same GIS. Results demonstrated two main landscape transformation trends: forest and semi-natural areas increased (8%), whereas agricultural areas decreased (12%). The urban area was relatively small during the entire analysed period, growing from 1% in 1954, to just 5% in 1992. Forest coverage was significant on reliefs, on hillside ecosystems such as limestone and on clay and marl hills LF. Arable land was particularly significant in flat ecosystems with deeper soils, such as on recent alluvial plain LF. These temporal changes were interpreted as being related to the replacement of traditional farming methods (grazing pastures) with more intensive methods (crop fields), especially on alluvial plains.     

湘赣边境志留纪花岗岩岩石谱系单位特征及侵位机制探讨

湘赣边境中段出露有黄洋界、汤湖两处花岗岩超单元和同时期侵位的岩株、岩瘤，其成岩时代属志留纪，按其侵位构造、岩石组合、地球化学等特征，可建立5个岩石单元，归并为汤湖超单元。岩浆的侵位受区域性大断裂左旋剪切应力控制．空间上呈现由南东往北西逐渐变新的特点。     

基于空间聚类的中国东北气候分区及其气象干旱时间变化特征

近50年来,中国东北地区干旱化趋势明显,越来越频繁的干旱事件对该区域人民的生产和生活造成了严重的影响。基于1961-2016年中国地面降水和气温月值0.5°×0.5°格点数据,利用空间系统聚类方法对中国东北进行气候分区,通过标准化降水指数探讨了中国东北各亚区气象干旱的时间演变规律,并结合小波功率谱和小波全谱分析了各亚区干旱的周期变化特征。结果表明：（1）空间上,可将东北地区分为8个气候亚区,即西辽河平原半干旱农牧区、吉辽东部山地湿润农林区、呼伦贝尔高原半干旱牧业区、东北中部半湿润—半干旱农牧区、黑吉东部山地湿润农林区、大兴安岭北部湿润林业区、呼伦贝尔—黑河湿润—半湿润农林区、三江平原湿润农业区;（2）不同亚区的干旱事件时间演变特征存在明显差异,但大致都经历了3个干旱时期：1960s后期、1970s中后期至1980s前期、1990s后期至2000s;2000-2010年间东北地区干旱发生频率和影响范围最大,尤其是中、西部;（3）不同亚区干旱变化主要存在11年和3~8年的显著周期,第一主周期差异明显。研究结果对于弄清楚中国东北各亚区特有的气候变化驱动因素、制定气候均质区域干旱监测计划和水资源的管理具有重要的现实意义。     

Spatially constrained clustering and upper level set scan hotspot detection in surveillance geoinformatics

We discuss upper level set (ULS) scan as a type of spatially constrained clustering in relation to two ways of imposing the spatial constraint, retrospectively versus progressively. We show that ULS scan produces the same results both ways; whereas two popular clustering techniques, single-linkage and K-means, can yield different results when spatial constraints are imposed retrospectively versus progressively. The ULS scan approach examines spatially connected components of a tessellation as a threshold is moved from the highest level (value) in the data to the lowest level. When the variable of interest on the tessellation is a rate of incidence, then a significance test is available based on binomial or Poisson null models and Monte Carlo techniques. This is a common context for detecting hotspots of diseases in epidemiological work. We also discuss an approach for extending the univariate methodology to accommodate multivariate contexts. Received: September 2005 / Revised: February 2006 This material is based upon work supported by (i) the National Science Foundation under Grant No. 0307010, (ii) the United States Environmental Protection Agency under Grant No. CR-83059301 and (iii) the Pennsylvania Department of Health using Tobacco Settlement Funds under Grant No. ME 01324. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the agencies.     

Modeling and spatial prediction of pre-settlement patterns of forest distribution using witness tree data

At the time of European settlement, land surveys were conducted progressively westward throughout the United States. Outside of the original 13 colonies, surveys generally followed the Public Land Survey system in which trees, called witness trees, were regularly recorded at 1 mi by 1 mi grid intersections. This unintentional sampling provides insight into the composition and structure of pre-European settlement forests, which is used as baseline data to assess forest change following settlement. In this paper, a model for the Public Land Surveys of east central Alabama is developed. Assuming that the locations of trees of each species are realized from independent Poisson processes whose respective log intensities are linear functions of environmental covariates (i.e., elevation, landform, and physiographic province), the species observed at the survey grid intersections are independently sampled from a generalized logistic regression model. If all 68 species found in the survey were included, the model would be highly over-parameterized, so only the distribution of the most common taxon, pines, will be considered at this time. To assess the impact of environmental factors not included in the model, a hidden Gaussian random field shall be added as a random effect. A Markov Chain Monte Carlo algorithm is developed for Bayesian inference on model parameters, and for Bayes posterior prediction of the spatial distribution of pines in east central Alabama. Received: June 2004 / Revised: November 2004     

Bayes methods for combining disease and exposure data in assessing environmental justice

Environmental justice reflects the equitable distribution of the burden of environmental hazards across various sociodemographic groups. The issue is important in environmental regulation, siting of hazardous waste repositories and prioritizing remediation of existing sources of exposure. We propose a statistical framework for assessing environmental justice. The framework includes a quantitative assessment of environmental equity based on the cumulative distribution of exposure within population subgroups linked to disease incidence through a dose-response function. This approach avoids arbitrary binary classifications of individuals solely as 'exposed' or 'unexposed'. We present a Bayesian inferential approach, implemented using Markov chain Monte Carlo methods, that accounts for uncertainty in both exposure and response. We illustrate our method using data on leukaemia deaths and exposure to toxic chemical releases in Allegheny County, Pennsylvania.     

A COMPLEMENTARY TOOL TO WATER QUALITY INDEX: FUZZY CLUSTERING ANALYSIS1

ABSTRACT: A general methodology for fuzzy clustering analysis is developed and illustrated with a case study of water quality evaluation for Dianshan Lake, Shanghai, China. Fuzzy clustering analysis may be used whenever a composite classification of water quality incorporates multiple parameters. In such cases, the technique may be used as a complement or an alternative to comprehensive assessment. In fuzzy clustering analysis, the classification is determined by a fuzzy relation. After a fuzzy similarity matrix has been established and the fuzzy relation stabilized, a dynamic clustering chart can be developed. Given a suitable threshold, the appropriate classification is worked out. The methodology is relatively simple, and the results can be interpreted to provide valuable information to support decision making and to aid water quality management.     

Estimating the risk of insect species invasion: Kohonen self-organising maps versus k-means clustering

Previous work on the estimation of the invasiveness of insect pest species used a single Kohonen self-organising map (SOM) to quantify the invasion potential of each member of a set of species in relation to a particular geographic region. In this paper that method is critically compared to an alternative approach of calculating the invasive potential of insect pest species as an outcome of clustering of regional species assemblages. Data clustering was performed using SOM and k-means optimisation clustering and multiple trials were performed with each algorithm. The outcomes of these two approaches were evaluated and compared to the previously published results obtained from a single SOM. The results show firstly, due to the inherent variation between trials of the algorithms used, that multiple trials are necessary to determine reliable risk ratings, and secondly, that k-means clustering can be considered a more appropriate algorithm for this particular application, as it produces clusters of higher quality, as determined by objective cluster measures, and is far more computationally efficient than SOM.